An Improved Ensemble Extreme Learning Machine Based on ARPSO and Tournament-Selection

Abstract

Extreme learning machine (ELM) performs more effectively than other learning algorithm in many cases, it has fast learning speed, good generalization performance and simple setting. However, how to select and cluster the candidate are still the most important issues. In this paper, KGA-ARPSOELM, an improved ensemble of ELMs based on K-means, tournament-selection and attractive and repulsive particle swarm optimization (ARPSO) strategy is proposed to obtain better candidates of the ensemble system. To improve classification and selection ability in the ensemble system, K-means is applied to cluster the ELMs efficiently while tournament- selection is used to choose the optimal base ELMs with higher fitness value in proposed method. Moreover, experiment results verify that the proposed method has the advantage of being more convenient to get better convergence performance than the traditional algorithms.